An efficient raindrop parameter estimation using image processing

P. Ghonge, K. Tuckley
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Abstract

Nowadays, image processing algorithms play a key role in the rain drop distribution estimation. This paper deals with number of drops and drop size distribution and its volume in particular time. We are using the raindrop image to calculate the amount of rainfall in a particular time. The proposed image processing based rain drop parameter estimation (IPRDPE) by using double-density dual-tree DWT (DDDT DWT) and thresholding based segmentation. By using effective image fusion technique, rain drop images from different angles are fused and using segmentation and morphological operations raindrop parameters estimated. To get better fused output max-based effective image fusion rules are used. The system using advanced image fusion technique and estimation for rain drop parameter, produce more accuracy and error free system compared to the existing techniques and also achieved better accuracy with respect to the real-time measurement.
一种基于图像处理的雨滴参数估计方法
目前,图像处理算法在雨滴分布估计中起着关键作用。本文研究了液滴数、液滴大小分布及其在特定时间内的体积。我们正在使用雨滴图像来计算特定时间的降雨量。提出了基于图像处理的雨滴参数估计(IPRDPE)方法,采用双密度双树DWT (DDDT DWT)和基于阈值分割。采用有效的图像融合技术,对不同角度的雨滴图像进行融合,并通过分割和形态运算估计雨滴参数。为了得到更好的融合输出,采用了基于最大值的有效图像融合规则。该系统采用先进的图像融合技术和雨滴参数估计,与现有技术相比,系统精度更高,无误差,在实时测量方面也达到了更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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